Plotting tips

%config InlineBackend.figure_format='retina'
from spacepy import pycdf
import math as mt
import matplotlib.pyplot as plt
import numpy as np
import scipy.fft as sf
import datetime
import bisect
import matplotlib.patches as patches
import matplotlib.dates as mdates
from IPython.display import display,Math
import matplotlib as mpl
from ai import cdas
import datetime
from matplotlib import pyplot as plt
import pandas as pd
from matplotlib.gridspec import GridSpec
import matplotlib.animation as animation

Observational data (for later used)

Use the cdas package to obtain data.

### for observation ###
data = cdas.get_data(
    'sp_phys',
    'STA_L1_MAG_RTN',
    datetime.datetime(2010, 1, 1, 0,  0,  0),
    datetime.datetime(2010, 1, 1, 1,  0,  1),
    ['BFIELD'],
    cdf=True # download data in CDF format
)

### convert to the dataframe of pandas ###
d = {'BR': data['BFIELD'][:,0], 'BT': data['BFIELD'][:,1], 'BN':data['BFIELD'][:,2]}
df0 = pd.DataFrame(data=d,index=data['Epoch'][:])
df0.index.name='time'
##########################################

plot parameters

You need to define these different from the cell which called matplotlib.pyplot

color='#3f3f3f'
mpl.rcParams['font.family'] = 'Arial'
mpl.rcParams['font.size']=14
mpl.rcParams['figure.dpi']=120
mpl.rcParams['axes.linewidth'] = 1.5
mpl.rcParams['xtick.major.size']=5
mpl.rcParams['xtick.minor.size']=4
mpl.rcParams['ytick.major.size']=5
mpl.rcParams['ytick.minor.size']=3
mpl.rcParams['xtick.major.width']=1
mpl.rcParams['ytick.major.width']=1
#mpl.rcParams['mathtext.default'] = 'regular'
#mpl.rcParams['xtick.top'] = 'True'
#mpl.rcParams['ytick.right'] = 'True'
mpl.rcParams['xtick.direction'] = 'out'
mpl.rcParams['ytick.direction'] = 'out'
#mpl.rcParams['axes.grid'] = 'True'
#mpl.rcParams['axes.xmargin'] = '0' #'.05'
#mpl.rcParams['axes.ymargin'] = '.05'
#mpl.rcParams['savefig.facecolor'] = 'None'
#mpl.rcParams['savefig.edgecolor'] = 'None'
mpl.rcParams['savefig.bbox'] = 'tight'
mpl.rcParams['axes.edgecolor']=color
mpl.rcParams['xtick.color']   =color
mpl.rcParams['ytick.color']   =color
mpl.rcParams['axes.labelcolor']=color
mpl.rcParams['animation.html'] = 'jshtml'

Ticks for both sides

fig, ax = plt.subplots(nrows=1,ncols=1,figsize=(6,6),sharex='all',dpi=80)

ax.xaxis.set_tick_params(which='major',  top='on')
ax.yaxis.set_tick_params(which='both' ,right='on')

ax.set_ylim(0.1,10)
ax.set_yscale('log')
ax.set_xlabel(r'x$\alpha$a')
plt.show()

fig, ax = plt.subplots(nrows=3,ncols=1,figsize=(6,6),sharex='all',dpi=80)

plt.subplots_adjust(hspace=0.)

for ic in range(3):
    ax[ic].xaxis.set_tick_params(which='major',  top='on', direction='in', length=5, width=2)

plt.show()

Heat map with log-scaled bins

It is better to use 'pcolormesh' rather than 'imshow'

x=np.linspace(0,2*np.pi,1000)
y=np.logspace(-1,1,1000)
X,Y=np.meshgrid(x,y)

Z1=np.cos(X)*np.sin(Y)

fig,ax=plt.subplots(figsize=(16,4))

im1=ax.pcolormesh(X,Y,Z1,cmap='jet',shading='auto')
ax.set_yscale('log')
fig.colorbar(im1,ax=ax,label='test',pad=0.02)

<matplotlib.colorbar.Colorbar at 0x11dec7590>

Shaded region over several panels

fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 4))

ax[2].set_xlim(0,10)

### add a rectangle patch ###
rect = patches.Rectangle((2, 0), 3, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False,transform=ax[2].get_xaxis_transform())
ax[2].add_patch(rect)
#############################
plt.show()


### date time format ###
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 4))
ax[2].plot(df0.index[:], df0['BR'][:])

### add a rectangle patch ###
#start='2010-01-01 00:20:00'
#end  ='2010-01-01 00:30:00'
#startplt = mdates.date2num(start)
#endplt   = mdates.date2num(end)
#rect = patches.Rectangle((startplt, 0), endplt-startplt, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False, transform=ax[2].get_xaxis_transform())

start = datetime.datetime(2010, 1, 1, 0,20,0)
end   = datetime.datetime(2010, 1, 1, 0,30,0)
rect = patches.Rectangle((start, 0), end-start, 3+2*fig.subplotpars.hspace, color='grey',alpha=.25, clip_on=False, transform=ax[2].get_xaxis_transform())
ax[2].add_patch(rect)
#############################

#ax[2].set_xlim(start,end)

plt.show()

/Users/mnakanot/anaconda3/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:102: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.

To register the converters:
	>>> from pandas.plotting import register_matplotlib_converters
	>>> register_matplotlib_converters()
  warnings.warn(msg, FutureWarning)

Vertical line over several panels

fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 4))
ax[2].set_xlim(0,10)
ax[2].axvline(4, 0, 3+2*fig.subplotpars.hspace, ls='dashed', color='k', clip_on=False)
plt.show()

### for observation ###
fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(8, 4))
start = datetime.datetime(2010, 1, 1, 0,20,0)
ax[2].plot(df0.index[:], df0['BR'][:])
ax[2].axvline(start, 0, 3+2*fig.subplotpars.hspace, ls='dashed', color='k', clip_on=False)
plt.show()

Crossed axes and equal aspect

fig,ax=plt.subplots(nrows=1,ncols=1,figsize=(8,4))

# Move left y-axis and bottim x-axis to centre, passing through (0,0)
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('center')

# Eliminate upper and right axes
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')

# Show ticks in the left and lower axes only
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')

ax.set_xlim(-5,20)
ax.set_ylim(-5,5)

ax.set_aspect('equal') 

plt.show()

Log scale color map

x=np.linspace(0,1,1000)
y=np.linspace(-1,1,1000)
X,Y=np.meshgrid(x,y)

Z1=np.abs(X*Y)

fig,ax=plt.subplots(figsize=(8,4))

im1=ax.imshow(Z1,cmap='jet',aspect='auto',origin='lower',extent=[x[0],x[-1],y[0],y[-1]], norm=mpl.colors.LogNorm(vmin=1e-3,vmax=1e0))
fig.colorbar(im1,ax=ax,label='test',pad=0.02)
plt.show()

Colorbar scale of date

#### plot figure ###
fig, ax = plt.subplots(ncols=1, figsize=(6,6))

N_TICKS = 12
cmap = plt.cm.get_cmap('jet', N_TICKS)    # 11 discrete colors

smap = ax.scatter(df0['BR'][:],df0['BT'][:],c=df0.index,cmap=cmap,marker='o',alpha=0.8)

smap.set_facecolor("none")

indexes = [df0['2010-01-01 00:00'].index[0], df0['2010-01-01 00:15'].index[0], df0['2010-01-01 00:30'].index[0], df0['2010-01-01 00:45'].index[0], df0['2010-01-01 01:00'].index[0]]

cb = fig.colorbar(smap, ax=ax, orientation='vertical', ticks= df0.loc[indexes].index.astype(int), label='hh:mm')

cb.ax.set_yticklabels([index.strftime('%H:%M') for index in indexes])

ax.set_xlabel(r'$B_R$ [nT]')
ax.set_ylabel(r'$B_T$ [nT]')

plt.show()

X label time format

### plot figure ###
fig, ax = plt.subplots(ncols=1, figsize=(16,4))

ax.plot(df0.index, df0['BR'][:])

myfmt=mdates.DateFormatter('%H:%M:%S\n%d %b %Y')
ax.xaxis.set_major_formatter(myfmt)

plt.show()

Different size panels

fig7, f7_axs = plt.subplots(ncols=3, nrows=3)
gs = f7_axs[1, 2].get_gridspec()
# remove the underlying axes
for ax in f7_axs[1:, -1]:
    ax.remove()
axbig = fig7.add_subplot(gs[1:, -1])

fig7.tight_layout()

widths = [2, 3, 1]
heights = [1, 3, 2]
#gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig6, f6_axes = plt.subplots(ncols=3, nrows=3, figsize=(6,4), constrained_layout=True,gridspec_kw={'width_ratios':widths, 'height_ratios':heights}) #or gridspec_kw=gs_kw

Mixed cartesian and Polar plots

### read the H number distribution ###
r =np.linspace(0, 70,100)
th=np.linspace(0,360,200,endpoint=True)/180*np.pi
R,Th=np.meshgrid(r,th)

### plot figure ###
#fig,ax=plt.subplots(nrows=2,figsize=(10,12))
fig=plt.figure(figsize=(12,3),dpi=80)

#plt.rcParams['font.size'] = 10

gs=GridSpec(1,3) # 2 rows, 3 columns
gs.update(wspace=0.1) # set the spacing between axes. 

ax0=fig.add_subplot(gs[0,0:2]) # First row, first column
ax1=fig.add_subplot(gs[0,2], polar=True) # First row, second column


#fig.add_subplot(121)
#ax0.plot(mu,label='monthly')
#ax0.plot(mu.rolling("8760D").mean(),label='24 year')
#ax0.legend()
ax0.grid()
ax0.set_xlabel('time [year]')
ax0.set_ylabel(r'$\mu$')
#ax0.set_xlim(mu.index[154],mu.index[634])

#ax=fig.add_subplot(122, polar=True,facecolor='grey')
#CS=ax.pcolormesh(Th,R,data,cmap=plt.cm.get_cmap('coolwarm', 10))
#fig.colorbar(CS, ax=ax,shrink=0.8,ticks=np.linspace(0,0.1,11),label='$n_H$ [cm$^{-3}$]')
#CS=ax1.pcolormesh(Th,R,data2,cmap='coolwarm')
#fig.colorbar(CS, ax=ax1,shrink=1,label='$n_H$ [cm$^{-3}$]')
#ax1.plot(P10rphi[::2000]['phi']-np.pi, P10rphi[::2000]['R'],'k-')
#ax1.plot(V1rphi[::1000]['phi']-np.pi, V1rphi[::1000]['R'],'k-.')
#ax1.plot(V2rphi[::100]['phi']-np.pi, V2rphi[::100]['R'],'k--')
#ax1.grid(False)
#ax.clabel(CS, inline=1, fontsize=10)
#ax.set_yticklabels([])
#ax1.thetagrids([theta * 15 for theta in range(360//15)])
#ax1.set_rmax(70)
#ax1.set_rlabel_position(135/2)
#import matplotlib.text as txt
#ax1.text(177*np.pi/180,60,'P10')
#ax1.text(-10*np.pi/180,60,'V1')
#ax1.text( 30*np.pi/180,60,'V2')
#ax1.text(135/2*np.pi/180,82,'[au]')
#plt.colorbar()
#ax.grid(True)
#cmap = cm.get_cmap('PiYG', 11)    # 11 discrete colors
#ax.set_thetamin(0)
#ax.set_thetamax(180)
#fig.tight_layout()
plt.show()

Scatter points over a line

x=np.arange(200)
y=np.random.randn(200)
z=np.zeros(200)
plt.plot(x,z, 'red', lw=5)
plt.scatter(x,y, zorder=2.5)
plt.show()

Subplots including a heatmap and a line plot.

Also setting gridspec_kw and width_ratios, you can adjust the size of figures.

xx=np.linspace(0,100*np.pi,1000)
yy=np.linspace(0,  2*np.pi,1000)
yy=np.cos(xx/4)*np.exp(-xx/100)
XX,YY=np.meshgrid(xx,yy)
ZZ=np.cos(XX/4)*np.exp(-XX/100)

fig, ax = plt.subplots(nrows=3, sharex=True, figsize=(16, 8))
plt.subplots_adjust(hspace=0.)

im0=ax[0].pcolormesh(XX,YY,ZZ,shading='nearest')
im1=ax[1].plot(xx,yy)
im2=ax[2].pcolormesh(XX,YY,ZZ*1e5,cmap='jet',shading='nearest')

fig.colorbar(im0,ax=ax[0],label='test'            ,aspect=5, pad=0.01)
fig.colorbar(im2,ax=ax[2],label='test\ntest\ntest',aspect=5, pad=0.01)

cb = fig.colorbar(im0,ax=ax[1],aspect=5, pad=0.01)
cb.ax.set_visible(False)

plt.show()

widths = [2, 2, 2]
heights = [1, 1]
gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig, ax = plt.subplots(ncols=3, nrows=2, constrained_layout=True,gridspec_kw=gs_kw)
Z=np.random.randn(20,20)
im1=ax[1,2].imshow(Z, aspect='auto')
fig.colorbar(im1, ax=ax[1,2])
plt.show()

Multiple axes

x=np.arange(0.01,np.e,0.01)
y1=np.exp(-x)
y2=np.log(x)
fig,ax=plt.subplots(figsize=(8,4))
ax.plot(x,y1)
ax.set_ylabel('Y values for exp(-x)')
ax.set_xlabel('Same X for both exp(-x) and ln(x)')

ax2=ax.twinx()
ax2.plot(x,y2,'r')
ax2.set_xlim([0,np.e])
ax2.set_ylim([-5,1])
ax2.set_ylabel('Y values for ln(x)', color='r')
ax2.tick_params(axis='y', colors='r')
ax2.spines["right"].set_edgecolor('r')

ax3=ax.twinx()
ax3.spines["right"].set_position(("axes", 1.12))# move the spine of the second axes outwards
ax3.plot(x, np.sin(4*x),'g')
ax3.set_xlim([0,np.e])
ax3.set_ylim(-1,1)
ax3.set_ylabel('Y values for ln(x)', color='g')
ax3.tick_params(axis='y', colors='g')
ax3.spines["right"].set_edgecolor('g')

plt.show()

Animation of heatmaps

def update_anim(it):
    
    fig.clf() #clear the figure
    
    ax=fig.subplots(2, 2) #add subplots
    
    fig.tight_layout() #reduce spacing around the figure

    #for i in range(2):
    #    for j in range(2):
    #        ax[i,j].cla()  
                    
    Z1=np.random.randn(N,N) #random numbers in 2D
    Z2=np.random.randn(N,N) #random numbers in 2D
    Z3=np.random.randn(N,N) #random numbers in 2D
    Z4=np.random.randn(N,N) #random numbers in 2D
    
    im1=ax[0,0].imshow(Z1 ,aspect='equal', origin='lower', cmap='Greys')
    im2=ax[1,0].imshow(Z2 ,aspect='equal', origin='lower', cmap='Blues')
    im3=ax[0,1].imshow(Z3 ,aspect='equal', origin='lower', cmap='Reds')
    im4=ax[1,1].imshow(Z4 ,aspect='equal', origin='lower', cmap='Greens')
    #im4=ax[1,1].plot(x,np.sin(it*x))
    
    cb1=fig.colorbar(im1, ax=ax[0,0])
    cb2=fig.colorbar(im2, ax=ax[1,0])
    cb3=fig.colorbar(im3, ax=ax[0,1])
    cb4=fig.colorbar(im4, ax=ax[1,1])

N=20
nt=20
x = np.linspace(-5, 5, N)
y = np.linspace(-5, 5, N)
X, Y = np.meshgrid(x, y)

fig=plt.figure(figsize=(8,8))
anim=animation.FuncAnimation(fig,update_anim,frames=nt)
plt.close()
anim
#anim.save('sample.gif', writer='imagemagick') #save the animation as a gif file

Once Loop Reflect

Hatched region

x=np.linspace(0,10,1000)
y1=np.zeros(1000)
y2=x**2/(1+x**2)
y3=x**(1/4)

fig, ax = plt.subplots()
ax.plot(x, y1, "k")
ax.plot(x, y2, "k")
ax.plot(x, y3, "k")

ax.fill_between(x, y1, y2, fc="w", hatch= "x") #{'/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
ax.fill_between(x, y2, y3, fc="w", hatch="//")
ax.set_ylim(0,2)

plt.show()

Figure in a figure

def f(x, y):
    return np.cos(x+y) ** 5 + np.cos(4 + y * x) * np.cos(-x)

x = np.linspace(0, 5, 100)
y = np.linspace(0, 5, 100)

X, Y = np.meshgrid(x, y)
Z = f(X, Y)

fig = plt.figure()
ax = plt.axes()
plt.imshow(Z, extent=[0, 5, 0, 5], origin='lower',
           cmap='gnuplot')
plt.colorbar()

axins = ax.inset_axes([0.45, 0.4, 0.5, 0.5])
axins.imshow(Z, extent=[0, 5, 0, 5],
          origin="lower",cmap='gnuplot')

# sub region of the original image
x1, x2, y1, y2 = 2, 3, 0.4, 1.4
axins.set_xlim(x1, x2)
axins.set_ylim(y1, y2)
axins.set_xticklabels('')
axins.set_yticklabels('')

ax.indicate_inset_zoom(axins)
plt.show()

x = np.arange(-10, 10, 0.1)
plt.plot(x, x**2) 

ax=plt.axes([0.5, 0.6, 0.2, 0.2]) 
ax.plot(x, x)
ax.set_xlim(-5,5)

plt.show()

Connect paths among panels

from matplotlib.patches import ConnectionPatch

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

# Draw a simple arrow between two points in axes coordinates
# within a single axes.
xyA = (0.2, 0.2)
xyB = (0.8, 0.8)
coordsA = "data"
coordsB = "data"
con = ConnectionPatch(xyA, xyB, coordsA, coordsB,
                      arrowstyle="->", shrinkA=5, shrinkB=5,
                      mutation_scale=20, fc="w")
ax1.plot([xyA[0], xyB[0]], [xyA[1], xyB[1]], "o")
ax1.add_artist(con)

# Draw an arrow between the same point in data coordinates,
# but in different axes.
xy = (0.0, 0.0)
coordsA = "data"
coordsB = "data"
con = ConnectionPatch(xyA=(0.3, 0.2), xyB=(0,0), coordsA=coordsA, coordsB=coordsB,
                      axesA=ax2, axesB=ax1,
                      arrowstyle="-", shrinkB=0)
ax2.add_artist(con)

# Draw a line between the different points, defined in different coordinate
# systems.
xyA = (0.6, 1.0)  # in axes coordinates
xyB = (0.0, 0.2)  # x in axes coordinates, y in data coordinates
coordsA = "axes fraction"
coordsB = ax2.get_yaxis_transform()
con = ConnectionPatch(xyA=xyA, xyB=xyB, coordsA=coordsA, coordsB=coordsB,
                      arrowstyle="-")
ax2.add_artist(con)

ax1.set_xlim(0, 1)
ax1.set_ylim(0, 1)
ax2.set_xlim(0, .5)
ax2.set_ylim(0, .5)

plt.show()

Contour plot and colorbar controll

x=np.linspace(-5,5,200)
y=np.linspace(-4,4,200)

X, Y = np.meshgrid(x,y)

nr=1000
cx=-10+20*np.random.rand(nr)
cy=-5 +10*np.random.rand(nr)
k=np.zeros(nr)
for i in range(nr):
    if cx[i]<0: k[i]=1
    if cx[i]>0: k[i]=2

Jz=np.zeros((200,200))
for i in range(nr):
    R=np.sqrt((X-cx[i])**2+(Y-cy[i])**2)
    Jz=Jz+2*k[i]/(1+(k[i]*R)**2)**2

fig,ax = plt.subplots(nrows=1, ncols=3, sharex=True, figsize=(16,8))
im0=ax[0].contour( X, Y, Jz, levels=40, colors='grey',alpha=0.4)
im1=ax[1].contour( X, Y, Jz, levels=40, cmap='jet')
im2=ax[2].contourf(X, Y, Jz, levels=20, cmap='jet')


from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable

divider = make_axes_locatable(ax[2])
cax = divider.append_axes("top", size="5%", pad=0.1)
cb2=fig.colorbar(im2, cax=cax, ax=ax[2], orientation='horizontal')
cax.xaxis.set_ticks_position('top')

divider1 = make_axes_locatable(ax[1])
cax1 = divider1.append_axes("top", size="5%", pad=0.1)
cb1=fig.colorbar(im1, cax=cax1, ax=ax[1], orientation='horizontal', pad=0.05)
cax1.xaxis.set_ticks_position('top')
#cb1.ax.set_visible(False)

divider0 = make_axes_locatable(ax[0])
cax0 = divider0.append_axes("top", size="5%", pad=0.1)
cb2=fig.colorbar(im0, cax=cax0, ax=ax[0], orientation='horizontal', pad=0.05)
cax0.xaxis.set_ticks_position('top')
cb2.ax.set_visible(False)

#fig.colorbar(im2, cax=cax, ax=ax[1], orientation='horizontal', pad=0.05, anchor=(0.5, 1.0))

for i in range(3):
    ax[i].set_aspect('equal')

plt.show()

plt.contourf(X,Y,Jz,levels=20,cmap='hot')
plt.contour( X,Y,Jz,levels=20,colors='white',linewidths=0.5)
plt.show()

Multicolored lines

Actually, there is no easy to plot it. Alternatively, you can use scatter (but points). If you really need multicolored lines, you can check here; [https://matplotlib.org/3.3.3/gallery/lines_bars_and_markers/multicolored_line.html]

x = np.linspace(0, 2*np.pi, 1000)
y = np.sin(10*x)*np.exp(-x)
z = np.sin(10*x)
fig, ax = plt.subplots()
im1=ax.scatter(x,y, s=10, c=z, edgecolor='none', cmap='bwr')
fig.colorbar(im1, ax=ax)
plt.show()

Several lines with a colorbar

### discrete ###
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)

#cmap = mpl.cm.get_cmap('jet', n_lines)
#fig, ax = plt.subplots(dpi=100)
## Make dummie mappable
#dummie_cax = ax.scatter(c, c, c=c, cmap=cmap)
## Clear axis
#ax.cla()
#for i, yi in enumerate(y.T):
#    ax.plot(x, yi, c=cmap(i))
#fig.colorbar(dummie_cax, ticks=c)
#plt.show();

cmap = plt.get_cmap("jet", len(c))
norm = mpl.colors.BoundaryNorm(np.arange(len(c)+1)+0.5,len(c))
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([])  # this line may be ommitted for matplotlib >= 3.1

fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
    ax.plot(x, yi, c=cmap(i))
fig.colorbar(sm, ticks=c)
plt.show()


### continuous ###
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)

norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])

fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
    ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();

Axis setting

np.random.seed(1234)
x   = np.linspace(0, 360, 1000)
x_a = np.linspace(0, 360, 12)
y   = 100*(1 - np.exp(-0.01*x))
y_a = 100*(1 - np.exp(-0.01*x_a)) + np.random.randn(12)

color='#545454'

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, 'k', lw=1.5, alpha=0.8, label='theory')
ax.plot(x_a, y_a, 'o', color='none', markersize=10, markeredgewidth=3, markeredgecolor=color, alpha=0.8, label='experiment')

ax.spines['top'   ].set_linewidth(0)
ax.spines['right' ].set_linewidth(0)
ax.spines['left'  ].set_linewidth(2)
ax.spines['bottom'].set_linewidth(2)

ax.spines['left'  ].set_color(color)
ax.spines['bottom'].set_color(color)

#ax.spines['right'].set_visible(False)
#ax.spines['left'].set_visible(False)
#ax.spines['top'].set_visible(False)
 
ax.tick_params(direction='out', length=6, width=2, colors=color, labelsize=14)

ax.set_xlim(0,370)
ax.set_ylim(0,105)

#ax.set_xticks([60*i for i in np.arange(7)])
#ax.set_yticks([0, 25, 50, 75, 100])

#ax.set_xticklabels([x for x in np.arange(7)], fontsize=14, color=color)
#ax.set_yticklabels([0,25,50,75, 100]        , fontsize=14, color=color)

#ax.xaxis.label.set_color('red')
#ax.tick_params(axis='x', colors='red')`1

ax.set_xlabel('time (h)'      , fontsize=14, color=color)
ax.set_ylabel('Conversion (%)', fontsize=14, color=color)
#plt.savefig('test.pdf')
plt.show()